This is where I find management strange that they do very little to install any confidence in the actual company itself self-promoting wise. I am under the impression that the company has the expertise to troubleshoot and collaborate the incorporation of Brianchips suite of technology into potential customer products.Hi Paxs.
I too would love to see our tech shining and improving capabilities where there is both a current need and where we have obvious advantages over extant systems.
I have long thought we need to show the world something that knocks their sox off, some killer app that showcases our advantages in a clear and easily understandable fashion, in order that we may both gain traction and establish our reputation broadly, both for rapid sustained growth and for brand recognition.
I had originally thought that would be in some medical diagnostic kit, such as a cheap, reliable and data protective Covid detector, which would have ridden the coattails of the unfortunate Pandemic into global consciousness.
Therefore I like your idea of us being involved in the hearing aid sector and dream of us being partnered with Cochlear or a similar entity, with both the existing good reputation and commercial heft to drive us forward.
The other immediate area we could enhance is drones/robotics and I would like to see us widely utilised here in some fashion where our characteristics should allow and showcase just how our hardware/software can vastly improved efficiency, battery endurance and the autonomy of even existing systems. I would love us to, again, be involved with a player such as Boston Dynamics or Tesla, who can market what we bring to the table, effectively. Maybe, this then provides the entree into a Samsung or Hyundai conglomerate so we begin to experience both scale and ubiquity.
I guess this is really retail investor thinking, largely driven with the impetus of an increasing share price, whilst the reality may turn out very differently, with a long, slow, quiet progression, largely hidden behind closed doors, defense procurement initiatives and NDA's and enmeshed within other's trademarks and brands.
For personal reasons, I'm still hoping for an announcement this week coming, and have been waiting for such, since October 2015, when I bought my first parcel of 10,000 shares for .325 cents.
Oh baby, what a ride it's been.
Bring It BrainChip!
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Here my lack of technological smarts will show how much of a novice I am. But I would be thinking by now they would have the ability to be able to demonstrate examples in rudimentary ways what is achievable on their platform without naming or disclosing anything covered by NDA agreements.
HopalongPetrovski
I'm Spartacus!
It’s not trying to sell hamburgers to the masses. The people they are trying to convince is a small select group of influential individuals that have the decision making ability to incorporate our technology into their’s. And the upcoming generation who are currently studying at certain universities.
So, they target trade shows and publications attended by and read by said people.
It’s a long slog unfortunately and the luck of what happens in the wider world is also a big factor which is largely beyond their control.
My desire for our amalgamation within some killer application is merely in the belief that some form of positive brand recognition would be helpful for all the same reasons that the Apple’s and Tesla’s have pursued in the past.
Have a look at the 2024 AGM video from circa 34 minute mark. Sean and the BOD fully understand there is frustration among holders about deal delays.
Sean explains why there are delays and shares his reasons for being confident and optimistic about engagements and wins.
These issues are also addressed in Question time.
IMO PICO will further enhance the value of our patent portfolio which is likely many times our current value.
rgupta
Regular
Results are not too far off!!!!
I suppose my real concern is another Australian company redomicile or taken over just because the people not in the know are kept in the dark and manipulation is used to force an agenda. I think the board can do more and should do more to address this. A simple annual share distribution report would go a long way. Doesn't matter if it good or bad people have a right to make informed decisions. If anyone can post a share distribution report later then 2022 would be an added bonus.
Good morning all,
Our "gun researchers/posters" may have already posted this, but the link below contains Steve Brightfields 18 slide presentation
at the recent AI Hardware & Edge Summit...if this hasn't already been posted, well then, enjoy reading these slides.
Once again, ANY shareholder who can't see the writing on the wall, as in, we are moving forward week by week, well then you
need to research our company more and absorb what you are actually reading, including what other experts say about our
technology and the future direction of AI at the "far edge".....Love our company and all our brilliant staff.....Tech x
Our "gun researchers/posters" may have already posted this, but the link below contains Steve Brightfields 18 slide presentation
at the recent AI Hardware & Edge Summit...if this hasn't already been posted, well then, enjoy reading these slides.
Once again, ANY shareholder who can't see the writing on the wall, as in, we are moving forward week by week, well then you
need to research our company more and absorb what you are actually reading, including what other experts say about our
technology and the future direction of AI at the "far edge".....Love our company and all our brilliant staff.....Tech x
buena suerte :-)
BOB Bank of Brainchip
Hey Hoppy,Hi Paxs.
I too would love to see our tech shining and improving capabilities where there is both a current need and where we have obvious advantages over extant systems.
I have long thought we need to show the world something that knocks their sox off, some killer app that showcases our advantages in a clear and easily understandable fashion, in order that we may both gain traction and establish our reputation broadly, both for rapid sustained growth and for brand recognition.
I had originally thought that would be in some medical diagnostic kit, such as a cheap, reliable and data protective Covid detector, which would have ridden the coattails of the unfortunate Pandemic into global consciousness.
Therefore I like your idea of us being involved in the hearing aid sector and dream of us being partnered with Cochlear or a similar entity, with both the existing good reputation and commercial heft to drive us forward.
The other immediate area we could enhance is drones/robotics and I would like to see us widely utilised here in some fashion where our characteristics should allow and showcase just how our hardware/software can vastly improved efficiency, battery endurance and the autonomy of even existing systems. I would love us to, again, be involved with a player such as Boston Dynamics or Tesla, who can market what we bring to the table, effectively. Maybe, this then provides the entree into a Samsung or Hyundai conglomerate so we begin to experience both scale and ubiquity.
I guess this is really retail investor thinking, largely driven with the impetus of an increasing share price, whilst the reality may turn out very differently, with a long, slow, quiet progression, largely hidden behind closed doors, defense procurement initiatives and NDA's and enmeshed within other's trademarks and brands.
For personal reasons, I'm still hoping for an announcement this week coming, and have been waiting for such, since October 2015, when I bought my first parcel of 10,000 shares for .325 cents.
Oh baby, what a ride it's been.
Bring It BrainChip!
Very similar story matey
And then a 2 weeks later topped up at half the price
And topped up 'many' times when it hit below 10c
Hoping for a great few weeks ahead chippers
Good luck oh patient ones
C'mon Sean .... WE are waiting !!!!!!!!!!!!!!!
Food4 thought
Regular
I am loving the excitement and build up to what eventually will become the best stock on the market in Australia
And it will truly explod when we get into the American market
And it will truly explod when we get into the American market
Bravo
If ARM was an arm, BRN would be its biceps💪!
Is it more than just co-incidence that Raspberry Pi has a Pico Series as @Humble Genius alluded to yesterday?
We know that our team showed off AKIDA Pico on applications that detect keywords in speech and the example given in the article I posted yesterday was that this would be useful for voice assistance which wait for keywords to activate, which seems to be exactly what the Raspberry Pico requires.
Intentional or coincidental, only time will tell...
View attachment 70280
View attachment 70281
Raspberry Pi Pico 2: Improvements and details
(Bild: Raspberry Pi) Details about Raspberry Pi Pico 2, its features, improvements, and details thanks to the RP2350 chip.www.all-about-industries.com
What's this then? Picovoice...
NASA is going to use Picovoice in it's next-gen voice-controlled applications? Hmmmm...
And, Picovoice are working on something that will take PicoLLM to the next level? Hmmm...
July 23, 2024
Want to Run LLMs on the Edge?
by Max MaxfieldI’ve just heard something that left me flabbergasted. Seriously. I cannot recall the last time my flabber was quite this gasted. All I can say is that if you dare to read this column, your own flabber is in danger of joining mine, so this might be a good time for you to don clothing appropriate to the occasion.
Let’s start with the concept of generative AI (GenAI) models like ChatGPT and Stable Fusion. These are known as large language models (LLMs). LLMs usually run in the cloud; that is, on honking big servers in honking big data centers. Well, suppose I were to tell you that I know of a company that has come up with a way of taking LLMs and running them on low-power processors located at the edge where the “internet rubber” meets the “real-world road”? Even better, suppose I were to tell you that the company in question is making this technology available for us all to use for free? How’s your flabber feeling now.
I was just chatting with Alireza Kenarsari-Anhari, who is the CEO of Picovoice. Based in Canada (there seems to be a heck of a lot of high-technology coming out of Canada these days), the company was founded in 2018. Although this seems like yesterday, it’s a lifetime away in the context of GenAI (remember ChatGPT wasn’t presented to the world until 30 November 2022).
Picovoice started life as a voice AI company with a mission to accelerate the transition of voice AI from running in the cloud to running on edge devices like the Arduino, STM32, and Raspberry Pi Zero.
It turns out that the folks at Picovoice are really, really good at what they do. They originally targeted their solutions at hardware companies, but they quickly discovered that a lot of software companies were also interested in building natural speech capabilities into things like security systems and web browsers. Even NASA is going to use Picovoice technology in its next generation of voice-controlled space applications like spacesuits.
Since the guys and gals at Picovoice wanted to squeeze their technology onto the smallest of processors, they spent a lot of effort figuring out how to implement artificial neural networks (ANNs) very, very efficiently. They also created their own ANN architecture, because even TensorFlow Lite (TFLite) was too big and hairy for what they were doing, and things like TFLite for Microcontrollers wasn’t available at that time (that little scamp didn’t see the light of day until 2019). Furthermore, they also created their own runtime for running neural networks on any processor known to humankind. This is known as XPU, which stands for MPU, MCU, GPU, NPU, etc.
Now, this is where things start to get very interesting indeed. It turns out that if you have a small neural network with only a couple of million parameters (weights), then almost every parameter contributes equally to the accuracy of the model, and it doesn’t much matter where the parameter is in the network.
By comparison, once you start working with neural networks like LLMs with hundreds of billions of parameters, then not all parameters are created equal (which makes me want to paraphrase George Orwell by saying: “all parameters are equal, but some are more equal than others”). In this case, we discover that there is a relatively small number of parameters that are extremely important. We can think of these as the “aristocracy” of parameters. If you perturb these parameters even a tiny bit, they have the ability to make your world go pear-shaped.
Then there’s a bigger group we might think of as “middle-class” parameters. Although they’re important, it’s not fatal if you ruffle their feathers a bit. Finally, we meet the largest group of all, the “working class” parameters, which are not particularly important on an individual basis, but they’re useful to have around—otherwise nothing ends up getting done. To put this another way, this last group of parameters are not individually important, but they contribute to the accuracy of the overall model by their sheer number.
But wait, there’s more, because in addition to having more parameters, LLMs also have more layers. The neural network models we use for things like machine vision have tens of neural layers. By comparison, LLMs have hundreds of layers, but not all layers are of equal significance, and their importance changes depending on the model you are using.
As Alireza told me, “All this got us thinking there should be an algorithm that tells us how to allocate our resources among all these parameters. Almost like a triage.”
After a lot of work, the result is picoLLM, which is an end-to-end local large language model (LLM) platform that enables enterprises to build AI assistants running on-devices, on-premises, and in private clouds without sacrificing accuracy.
If you have a hardware platform with limited resources, like 1 gigabyte of RAM, for example, and you have an LLM with hundreds of layers and 10 billion parameters, for example, then picoLLM can analyze the LLM’s layers and the parameters, determine what’s most important, prune things down, and distribute what’s left across the available hardware resources. All this is extremely fine-grained. Some of the parameters become one bit, some become two bits, some become three bits, and so forth depending on how important they are. In a crunchy nutshell, picoLLM can take a humongous LLM and boil it down into something that will fit into your physical system.
As I mentioned earlier, the folks at Picovoice started as a voice AI company with a mission to accelerate the transition of voice AI from running in the cloud to running on edge devices like the Arduino, STM32, and Raspberry Pi Zero. Now they’ve expanded their mission to accelerate the transition from LLMs running in the cloud to running on the edge.
Obviously, Picovoice is a for-profit company, so why are the folks at Picovoice making their awesome picoLLM technology available for the rest of us to use for free?
Well, it must be acknowledged that Alireza sounded just a little smug when he told me that the guys and gals at Picovoice are in a lucky position in that their voice products are making money and the voice market is on the rise, so they don’t need to raise money and they don’t need investors.
When they started thinking about the next growth enabler, LLMs were the obvious choice. The chaps and chapesses at Picovoice were already good at making ANNs run efficiently with limited resources on the edge, and they realized that many LLMs need to run locally because of cost, privacy, latency, etc. issues.
As Alireza says: “Any cloud user we turn into an edge advocate is a win for us in the long term.” He also told me about the new technology they are working on—something that will take picoLLM to the next level—but my lips are sealed and that will be a topic for another day. In the meantime, do you have any thoughts you’d care to share on any of this?
Hey Hoppy,
Very similar story matey
View attachment 70398 View attachment 70402
And then a 2 weeks later topped up at half the price
View attachment 70400 View attachment 70401
And topped up 'many' times when it hit below 10c
Hoping for a great few weeks ahead chippers
Good luck oh patient ones
C'mon Sean .... WE are waiting !!!!!!!!!!!!!!!
Hey Sean and Team !!!!!!
OK,View attachment 70405
What's this then? Picovoice...
NASA is going to use Picovoice in it's next-gen voice-controlled applications? Hmmmm...
And, Picovoice are working on something that will take PicoLLM to the next level? Hmmm...
July 23, 2024
Want to Run LLMs on the Edge?
by Max Maxfield
I’ve just heard something that left me flabbergasted. Seriously. I cannot recall the last time my flabber was quite this gasted. All I can say is that if you dare to read this column, your own flabber is in danger of joining mine, so this might be a good time for you to don clothing appropriate to the occasion.
Let’s start with the concept of generative AI (GenAI) models like ChatGPT and Stable Fusion. These are known as large language models (LLMs). LLMs usually run in the cloud; that is, on honking big servers in honking big data centers. Well, suppose I were to tell you that I know of a company that has come up with a way of taking LLMs and running them on low-power processors located at the edge where the “internet rubber” meets the “real-world road”? Even better, suppose I were to tell you that the company in question is making this technology available for us all to use for free? How’s your flabber feeling now.
I was just chatting with Alireza Kenarsari-Anhari, who is the CEO of Picovoice. Based in Canada (there seems to be a heck of a lot of high-technology coming out of Canada these days), the company was founded in 2018. Although this seems like yesterday, it’s a lifetime away in the context of GenAI (remember ChatGPT wasn’t presented to the world until 30 November 2022).
Picovoice started life as a voice AI company with a mission to accelerate the transition of voice AI from running in the cloud to running on edge devices like the Arduino, STM32, and Raspberry Pi Zero.
It turns out that the folks at Picovoice are really, really good at what they do. They originally targeted their solutions at hardware companies, but they quickly discovered that a lot of software companies were also interested in building natural speech capabilities into things like security systems and web browsers. Even NASA is going to use Picovoice technology in its next generation of voice-controlled space applications like spacesuits.
Since the guys and gals at Picovoice wanted to squeeze their technology onto the smallest of processors, they spent a lot of effort figuring out how to implement artificial neural networks (ANNs) very, very efficiently. They also created their own ANN architecture, because even TensorFlow Lite (TFLite) was too big and hairy for what they were doing, and things like TFLite for Microcontrollers wasn’t available at that time (that little scamp didn’t see the light of day until 2019). Furthermore, they also created their own runtime for running neural networks on any processor known to humankind. This is known as XPU, which stands for MPU, MCU, GPU, NPU, etc.
Now, this is where things start to get very interesting indeed. It turns out that if you have a small neural network with only a couple of million parameters (weights), then almost every parameter contributes equally to the accuracy of the model, and it doesn’t much matter where the parameter is in the network.
By comparison, once you start working with neural networks like LLMs with hundreds of billions of parameters, then not all parameters are created equal (which makes me want to paraphrase George Orwell by saying: “all parameters are equal, but some are more equal than others”). In this case, we discover that there is a relatively small number of parameters that are extremely important. We can think of these as the “aristocracy” of parameters. If you perturb these parameters even a tiny bit, they have the ability to make your world go pear-shaped.
Then there’s a bigger group we might think of as “middle-class” parameters. Although they’re important, it’s not fatal if you ruffle their feathers a bit. Finally, we meet the largest group of all, the “working class” parameters, which are not particularly important on an individual basis, but they’re useful to have around—otherwise nothing ends up getting done. To put this another way, this last group of parameters are not individually important, but they contribute to the accuracy of the overall model by their sheer number.
But wait, there’s more, because in addition to having more parameters, LLMs also have more layers. The neural network models we use for things like machine vision have tens of neural layers. By comparison, LLMs have hundreds of layers, but not all layers are of equal significance, and their importance changes depending on the model you are using.
As Alireza told me, “All this got us thinking there should be an algorithm that tells us how to allocate our resources among all these parameters. Almost like a triage.”
After a lot of work, the result is picoLLM, which is an end-to-end local large language model (LLM) platform that enables enterprises to build AI assistants running on-devices, on-premises, and in private clouds without sacrificing accuracy.
If you have a hardware platform with limited resources, like 1 gigabyte of RAM, for example, and you have an LLM with hundreds of layers and 10 billion parameters, for example, then picoLLM can analyze the LLM’s layers and the parameters, determine what’s most important, prune things down, and distribute what’s left across the available hardware resources. All this is extremely fine-grained. Some of the parameters become one bit, some become two bits, some become three bits, and so forth depending on how important they are. In a crunchy nutshell, picoLLM can take a humongous LLM and boil it down into something that will fit into your physical system.
As I mentioned earlier, the folks at Picovoice started as a voice AI company with a mission to accelerate the transition of voice AI from running in the cloud to running on edge devices like the Arduino, STM32, and Raspberry Pi Zero. Now they’ve expanded their mission to accelerate the transition from LLMs running in the cloud to running on the edge.
Obviously, Picovoice is a for-profit company, so why are the folks at Picovoice making their awesome picoLLM technology available for the rest of us to use for free?
Well, it must be acknowledged that Alireza sounded just a little smug when he told me that the guys and gals at Picovoice are in a lucky position in that their voice products are making money and the voice market is on the rise, so they don’t need to raise money and they don’t need investors.
When they started thinking about the next growth enabler, LLMs were the obvious choice. The chaps and chapesses at Picovoice were already good at making ANNs run efficiently with limited resources on the edge, and they realized that many LLMs need to run locally because of cost, privacy, latency, etc. issues.
As Alireza says: “Any cloud user we turn into an edge advocate is a win for us in the long term.” He also told me about the new technology they are working on—something that will take picoLLM to the next level—but my lips are sealed and that will be a topic for another day. In the meantime, do you have any thoughts you’d care to share on any of this?
So that last paragraph does leave the door open for a software implementation, but we do not have an exclusive licence for NDAs.
It would be great to have an established AI software vendor as a licencee.
The thing is, they would also either need to use BRN SLMs or adapt their in-house SLMs for Akida, or, most probably, both.
Money for jam (or money for old rope, as my EE lecturer used to say in his broad Midlands accent - I never figured out if that was his daily breakfast. At least he had the jam with it.)
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